Parts-based segmentation with overlapping part models using Markov chain Monte Carlo

Seise, Matthias; McKenna, Stephen J.; Ricketts, Ian W.; Wigderowitz, C.

doi:10.1016/j.imavis.2008.04.020

Cited by 2 publications

(5 citation statements)

References 30 publications

(27 reference statements)

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“…Deformable algorithms morph a priori shapes to the image data, ensuring closed surfaces. 36,37 Active contouring 38 is another technique that is used in some studies for segmentation of bone images. 39 Each type of algorithm has its advantages and disadvantages, and from an application point of view, algorithms that belong to different methods are more complementary than competitive.…”

Section: Segmentation and Mesh Generationmentioning

confidence: 99%

Patient-specific finite element modeling of bones

Poelert

Valstar

Weinans

et al. 2012

Proc Inst Mech Eng H

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Finite element modeling is an engineering tool for structural analysis that has been used for many years to assess the relationship between load transfer and bone morphology and to optimize the design and fixation of orthopedic implants. Due to recent developments in finite element model generation, for example, improved computed tomography imaging quality, improved segmentation algorithms, and faster computers, the accuracy of finite element modeling has increased vastly and finite element models simulating the anatomy and properties of an individual patient can be constructed. Such so-called patient-specific finite element models are potentially valuable tools for orthopedic surgeons in fracture risk assessment or pre- and intraoperative planning of implant placement. The aim of this article is to provide a critical overview of current themes in patient-specific finite element modeling of bones. In addition, the state-of-the-art in patient-specific modeling of bones is compared with the requirements for a clinically applicable patient-specific finite element method, and judgment is passed on the feasibility of application of patient-specific finite element modeling as a part of clinical orthopedic routine. It is concluded that further development in certain aspects of patient-specific finite element modeling are needed before finite element modeling can be used as a routine clinical tool.

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Section: Segmentation and Mesh Generationmentioning

confidence: 99%

Patient-specific finite element modeling of bones

Poelert

Valstar

Weinans

et al. 2012

Proc Inst Mech Eng H

View full text Add to dashboard Cite

show abstract

“…Thus successful medical image segmentation cannot be achieved utilizing gray-level features alone. Literature in medical image segmentation consequently utilizes a-priori knowledge of the region of interest and augments that knowledge with graylevel segmentation algorithms [2][3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

“…A 2D/3D registration between the statistical model and the X-ray images is then employed to extract the femur contours. Literature work of [2,4,5] presents ideas based on 2D active shape models. In [2], a probabilistic segmentation methodology formulates explicit models of the overlapping object appearance and shape geometry from training images which are then used to segment the femur and tibia in knee X-ray images.…”

Section: Introductionmentioning

confidence: 99%

“…Literature work of [2,4,5] presents ideas based on 2D active shape models. In [2], a probabilistic segmentation methodology formulates explicit models of the overlapping object appearance and shape geometry from training images which are then used to segment the femur and tibia in knee X-ray images. Previous work conducted by the same authors has employed a simpler double contour 2D active shape model in order to simultaneously segment anterior and lateral contours of the knee region.…”

Section: Introductionmentioning

confidence: 99%

“…Literature presented by [2,10] employs segmented bone contours for osteoarthritis management and tracking. Here the contours are used to provide measurement of unimpaired joint space width.…”

Section: Introductionmentioning

confidence: 99%

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